A New Fuzzy Identification Method Based on Adaptive Critic Designs

This paper proposes an adaptive critic design-based event-triggered optimal control method for input-constrained continuous-time nonlinear systems. Adaptive critic design is a special framework of adaptive dynamic programming that approximates the value function by a critic neural network and derives the approximate optimal control policy through analytical methods. The proposed adaptive critic design considers the control input constraints by introducing a non-quadratic cost function and employs an event-triggered mechanism to reduce the number of controller executions. Unlike the existing event-triggered adaptive critic design, this paper proposes a novel finite-time adaptive law based on regression filtering scheme. The adaptive law utilizes the error information of the network weights to ensure fast convergence to the optimal control law under the event-triggered mechanism, which improves the real-time performance of the system. Additionally, explicit bounds for each parameter in the compact set and the specific convergence time estimates are provided in the convergence analysis. Finally, the effectiveness and practicality of the proposed method for real-time online applications are validated through two simulation examples.

The wastewater treatment process (WWTP) is of great significance to environmental protection. To improve the efficiency of the WWTP, it is crucial to ensure that the dissolved oxygen (DO) concentration tracks the set value efficiently. Due to the nonlinear and time-varying dynamics of the WWTP, traditional control methods cannot accurately control the DO concentration. To overcome these challenges, this paper proposes an online transferred heuristic dynamic programming (TrHDP) control design by combining transfer learning with adaptive critic design. First, we use the historical sample data to construct a mathematical model of the WWTP and learn the prior knowledge from the model. Then, the online control process of the DO concentration is guided by utilizing the prior knowledge. In order to avoid negative transfer and save computing resources, we design a novel decay function with the truncation mechanism. In addition, we prove the stability of the TrHDP control scheme by constructing a Lyapunov function. Finally, the performance of the TrHDP scheme is verified by the Benchmark Simulation Model No. 1. Compared with other methods, the TrHDP method possesses higher control accuracy for the DO concentration and overcomes the disadvantage of low learning efficiency of general online methods.

The design of fight control is full of challenges because of the complex model and the complicated fight environment. The traditional control methods no longer ensure the demand in the many fight modes. Advanced control methods attract more and more researchers to make application research in this field. Adaptive critic design (ACD) is an effective control method to learn the optimal control online. The performance of fight control with atmospheric disturbance needs to be improved using active control or robust control. This paper presents adaptive critic based controller design of aircraft with atmospheric disturbance. The controller does not rely on the parameters of the aircraft, and it can learn online very well. When there are atmospheric disturbances, such as random turbulence, discrete gust and wind shear, adaptive critic based controller can resist the disturbances effectively, and shows the good robust performance. Finally, the simulation proves the validity of the adaptive critic based controller for aircraft with atmospheric disturbance.

Nearly Optimal Control Scheme Using Adaptive Dynamic Programming Based on Generalized Fuzzy Hyperbolic Model

Optimal [formula omitted] tracking control of nonlinear systems with zero-equilibrium-free via novel adaptive critic designs

A PWA model identification method for nonlinear systems using hierarchical clustering based on the gap metric

Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data, Part I: Measurement of nonlinear dynamics

We study a model of evolving populations of self-learning agents and analyze the interaction between learning and evolution. We consider agent-brokers that predict stock price changes and use these predictions for selecting actions. Each agent is equipped with a neural network adaptive critic design for behavioral adaptation. We discuss three cases in which either learning, or evolution, or both, are active in our model. We show that the Baldwin effect can be observed in our model, viz., originally acquired adaptive policy of best agent-brokers becomes inherited over the course of the evolution. Additionally, we analyze influence of neural network structure of adaptive critic design on learning processes.

This paper presents the design of a neuro-controller for a turbogenerator using a novel technique based on adaptive critic designs (ACD). This adaptive critic design based neuro-controller augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor of the generator. Simulation results are presented to show that neural network controllers with the ACD have the potential to control turbogenerators when system conditions and configuration changes.

This paper presents a method for real-time tuning of cost functions in coordinated wide area controller for improved power system transient stability. The algorithm is an optimal Wide Area System-Centric Controller and Observer (WASCCO) based on Adaptive Critic Design (ACD). ACD techniques using reinforcement learning (RL) are able to approximate dynamic programming based nonlinear optimal control but, the cost function for the optimal control requires online tuning. Here cost function defined in ACD are analyzed online with Lyapunov stability function. Further an online tuning method is designed based on eigenvalues extracted based on Prony analysis and linked to performance index generation. Results on a two area system shows better response compared to conventional schemes.

Theory of nonlinear systems

Analysis of Nonlinear Systems with Stochastic Input and Stochastic Parameters—II

Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

The boiler combustion process of power plant is a typical process with the features of multi-input, multi-output, strong non-linearity, strong jamming and close coupling. The coupling relationship between its parameters correlated with combustion process is very anfractuous, so it is very hard to solve its optimal control problem with conventional control methods. And Adaptive Critic Designs (ACDs) is a good way to deal with the approximate optimal control problems over time in complex nonlinear systems. But most of the ACD structures were designed by BP (Back Propagation) neural network, the controllers are easy to fall into local minimum, so usually the learning efficiency is very low even fail to training. In order to speed up the learning process of the controllers, this paper try to design an optimal controller based on Dual Heuristic Programming (DHP) by Generalized Radial Basis Function Neural Network (GRBFNN) and applied it to the simulation control of the boiler combustion process. The results indicate that the designed controller is effective.

In the present paper on-line action-dependent heuristic neuro-dynamic programming was applied for optimization of a complex nonlinear production process. The approach is based on recurrent neural network architecture — Echo state network (ESN) — as critic network within the frame of adaptive critic design (ACD) scheme. The subject for optimization is bio-ethanol fermentation. The aim was to increase process productivity defining optimal dilution rate profile. The obtained by simulations results are good basis for further experimental investigations.